As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult
to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia
data, from which it is difficult to extract some conceptual description of their contents.
One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users
are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck.
As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present
an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank
algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely
on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis
step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.